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Algorithms for imputing partially observed recurrent events with applications to multiple imputation in pattern

Yongqiang Tang1

  • 1a Department of Biostatistics and Programming , Lexington , MA , USA.

Journal of Biopharmaceutical Statistics
|May 26, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces five algorithms for imputing recurrent event data, enhancing statistical analysis for clinical trials. These methods improve handling of partially observed data, particularly under non-ignorability assumptions.

Keywords:
Mixed poisson processnegative binomial processoverdispersionpattern mixture modelsrejection sampling

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Area of Science:

  • Statistics
  • Biostatistics
  • Clinical Trials

Background:

  • Recurrent event data analysis is crucial in clinical trials.
  • Partially observed data and non-ignorability assumptions pose analytical challenges.
  • Pattern mixture models are widely used for sensitivity analysis.

Purpose of the Study:

  • To develop and validate algorithms for imputing partially observed recurrent events.
  • To extend imputation methods to mixed Poisson processes with continuous and discontinuous mean functions.
  • To facilitate the application of pattern mixture models in clinical trial sensitivity analyses.

Main Methods:

  • Negative binomial and mixed Poisson processes are used to model recurrent events.
  • Five distinct imputation algorithms are described.
  • Simulation studies are conducted to assess algorithm validity.
  • Methods are extended to handle jump discontinuities in event rate functions.

Main Results:

  • The proposed algorithms effectively impute partially observed recurrent event data.
  • Simulations confirm the validity and robustness of the imputation methods.
  • The algorithms are applicable to both continuous and discontinuous event rate functions.
  • The imputation techniques are demonstrated using a chronic granulomatous disease trial.

Conclusions:

  • The developed imputation algorithms provide a valuable tool for analyzing recurrent event data in clinical trials.
  • These methods enhance the implementation of pattern mixture models for sensitivity analyses.
  • The approach addresses challenges posed by missing data under non-ignorability assumptions.
  • Accurate imputation of recurrent events is essential for reliable clinical trial outcomes.